A new dynamic deep learning noise elimination method for chip-based real-time PCR.

Point-of-care (POC) real-time polymerase chain reaction (PCR) has become one of the most important technologies for many fields such as pathogen detection and water-quality monitoring. POC real-time PCR usually adopts chips with small-volume chambers for portability, which is more likely to produce complex noise that seriously affects the accuracy. Such complex noises are difficult to be eliminated by the traditional fixed area algorithm that is most commonly used at present because they usually have random shape, location, and brightness. To address this problem, we proposed a novel image analysis method, Dynamic Deep Learning Noise Elimination Method (DIPLOID), in this paper. Our new method could recognize and output the mask of the interference by Mask R-CNN, and then subtract the interference and select the maximum valid contiguous area for brightness analysis by dynamic programming. Compared with the traditional method, DIPLOID increased the accuracy, sensitivity, and specificity from 57.9 to 94.6%, 49.1 to 93.9%, and 65.9 to 95.2%, respectively. DIPLOID has great anti-interference, robustness, and sensitivity, which can reduce the impact of complex noise as much as possible from the aspect of the algorithm. As shown in the experiments of this paper, our method significantly improved the accuracy to over 94% under the complex noise situation, which could make the POC real-time PCR have greater potential in the future.

A New Few-Shot Learning Method of Bacterial Colony Counting Based on the Edge Computing Device.

Bacterial colony counting is a time consuming but important task for many fields, such as food quality testing and pathogen detection, which own the high demand for accurate on-site testing. However, bacterial colonies are often overlapped, adherent with each other, and difficult to precisely process by traditional algorithms. The development of deep learning has brought new possibilities for bacterial colony counting, but deep learning networks usually require a large amount of training data and highly configured test equipment. The culture and annotation time of bacteria are costly, and professional deep learning workstations are too expensive and large to meet portable requirements. To solve these problems, we propose a lightweight improved YOLOv3 network based on the few-shot learning strategy, which is able to accomplish high detection accuracy with only five raw images and be deployed on a low-cost edge device. Compared with the traditional methods, our method improved the average accuracy from 64.3% to 97.4% and decreased the False Negative Rate from 32.1% to 1.5%. Our method could greatly improve the detection accuracy, realize the portability for on-site testing, and significantly save the cost of data collection and annotation over 80%, which brings more potential for bacterial colony counting.

A Rapid Digital PCR System with a Pressurized Thermal Cycler.

We designed a silicon-based fast-generated static droplets array (SDA) chip and developed a rapid digital polymerase chain reaction (dPCR) detection platform that is easy to load samples for fluorescence monitoring. By using the direct scraping method for sample loading, a droplet array of 2704 microwells with each volume of about 0.785 nL can be easily realized. It was determined that the sample loading time was less than 10 s with very simple and efficient characteristics. In this platform, a pressurized thermal cycling device was first used to solve the evaporation problem usually encountered for dPCR experiments, which is critical to ensuring the successful amplification of templates at the nanoliter scale. We used a gradient dilution of the hepatitis B virus (HBV) plasmid as the target DNA for a dPCR reaction to test the feasibility of the dPCR chip. Our experimental results demonstrated that the dPCR chip could be used to quantitatively detect DNA molecules. Furthermore, the platform can measure the fluorescence intensity in real-time. To test the accuracy of the digital PCR system, we chose three-channel silicon-based chips to operate real-time fluorescent PCR experiments on this platform.