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 E-Mail IMAGE: Deep learning helps researchers to find hidden features in resistive pulse signals in nanopore sensing of nanoscale objects. view more  Credit: Osaka University Osaka, Japan - Scientists from the Institute of Scientific and Industrial Research at Osaka University used machine learning methods to enhance the signal-to-noise ratio in data collected when tiny spheres are passed through microscopic nanopores cut into silicon substrates. This work may lead to much more sensitive data collection when sequencing DNA or detecting small concentrations of pathogens. Miniaturization has opened the possibility for a wide range of diagnostic tools, such as point-of-care detection of diseases, to be performed quickly and with very small samples. For example, unknown particles can be analyzed by passing them through nanopores and recording tiny changes in the electrical current. However, the intensity of these signals can be very low, and is often buried under random

Makusu tsutsuiTakashi washioIndustrial research at osaka universityOsaka universityInstitute of scientificIndustrial researchSmall methodsதொழில்துறை ஆராய்ச்சி இல் ஒசகக பல்கலைக்கழகம்ஒசகக பல்கலைக்கழகம்நிறுவனம் ஆஃப் அறிவியல்தொழில்துறை ஆராய்ச்சிசிறிய முறைகள்