Med Biol Eng Comput - GPU-based real-time detection and analysis of biological targets using solid-state nanopores.

Tópicos

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Resumo

The emergence of nanoscale devices has provided robust interfaces to biomolecules that faithfully transduce and define fundamental interactions of living systems. Measuring single-event behavior of important targets like DNA, and diseased cells has been achieved with a number of devices and systems. An important dimension to these systems, often discounted, is real-time computational decision-making from measured data. This paper describes an adaptive approach that can record single-molecule or single-cell events in real-time and automatically analyze patterns from the measured data. The automated analysis of measured data is done using a static threshold technique and two variations of a dynamic threshold technique: baseline-tracker and moving average filtering. Dynamic techniques for threshold detection enable noise suppression in the measured data and precise detection of patterns, but at the cost of more complex software as compared to static technique. To mitigate the computational overhead, a real-time system is implemented that uses advanced I/O techniques to minimize the execution stalls, thus enabling the system to process data significantly faster than the electrical measurement setup. Furthermore, the algorithms are implemented on programmable graphics processing units for parallel pattern detection. Our implementation provides five times faster data acquisition and pattern detection than the maximum sampling rate of the electrical measurement setup.

Resumo Limpo

emerg nanoscal devic provid robust interfac biomolecul faith transduc defin fundament interact live system measur singleev behavior import target like dna diseas cell achiev number devic system import dimens system often discount realtim comput decisionmak measur data paper describ adapt approach can record singlemolecul singlecel event realtim automat analyz pattern measur data autom analysi measur data done use static threshold techniqu two variat dynam threshold techniqu baselinetrack move averag filter dynam techniqu threshold detect enabl nois suppress measur data precis detect pattern cost complex softwar compar static techniqu mitig comput overhead realtim system implement use advanc io techniqu minim execut stall thus enabl system process data signific faster electr measur setup furthermor algorithm implement programm graphic process unit parallel pattern detect implement provid five time faster data acquisit pattern detect maximum sampl rate electr measur setup

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