Building the Computational Mind for the “Swiss Army Knife” of Microscopes
A versatile microscope developed in collaboration with Berkeley Lab combines advanced imaging techniques, corrects distortion in biological specimens, and generates enormous datasets — requiring new state-of-the-art AI models as the mind to interact with the data. The post Building the Computational Mind for the “Swiss Army Knife” of Microscopes appeared first on Berkeley Lab News Center .
Biology operates at various scales, from nanometers to millimeters, and understanding these complex systems requires simultaneous observation. However, specialized instruments often cause damage to samples and slow down processes. The Multimodal Optical Scope with Adaptive Imaging Correction (MOSAIC) aims to address these challenges by integrating over ten imaging techniques into one compact microscope.
Developed at Lawrence Berkeley National Laboratory (Berkeley Lab) with support from the Perlmutter supercomputer, MOSAIC can produce up to four terabytes of data per hour, far surpassing conventional processing capabilities. Berkeley Lab's expertise in high-performance computing and large-scale data analysis is crucial to extracting meaningful biological insights from this overwhelming amount of data.
MOSAIC's compact design allows for rapid reconfiguration between imaging modes, while adaptive optics technology corrects blurring caused by living tissue. This versatility has enabled a range of experiments, from tracking single molecules in living cells to observing neuronal architecture in human brain tissue. Despite its impressive capabilities, MOSAIC still faces a significant bottleneck in processing the massive datasets it generates.
Berkeley Lab's PetaKit5D software toolkit and computational resources at the Perlmutter supercomputer help to address this issue, but the challenge remains to turn these dense five-dimensional observations into biological understanding.
Written by urgent.news from Berkeley Lab's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
