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Droplet digital PCR (ddPCR) is a powerful method for absolute nucleic acid quantification with high precision and accuracy. However, complicated operational steps have hampered the use and diffusion of ddPCR. Therefore, an automated, easy-to-use, low-sample-consumption, and portable ddPCR platform is urgently needed. This paper proposes a microfluidic ddPCR platform based on a microfluidic chip that can realize the sample-to-result function by switching the rotary valve, achieving the dual function of the flow-focusing structure for droplet generation and readout. Sample, generation oil, and analysis oil were pre-added to the reservoirs. Droplets were generated due to focusing flow, and after passing through the integrated temporary storage bin in the rotary valve, the droplets and oil subsequently entered the collecting tube, improving the droplet-to-oil volume ratio for enhanced thermal cycle performance. Droplets with an average diameter of 107.44 μm and a CV of 2.38% were generated using our chip under the optimal pressures. High-performance thermal cycling was achieved through improvements of the droplet-to-oil volume ratio of the sample, the integrated heating lid, the pure copper heating base, and the temperature-controlling algorithm. Gradient quantification experiments were conducted for the HER2 and CEP17 genes extracted from breast cancer cells, yielding strong linear correlations with R2 values of 0.9996 for FAM and 0.9989 for CY5. Moreover, pronounced linearity was obtained between the detected concentrations of HER2 and CEP17, indicated by a slope of 1.0091 and an R2 of 0.9997, signifying consistent HER2 : CEP17 ratios across various sample dilutions. The outcomes of the quantitative analysis, encompassing the dynamic range and the consistency of the HER2 : CEP17 ratio using our ddPCR platform, meet the standards required for breast cancer assessment and therapy. Our ddPCR platform is automated, portable, and capable of stable droplet generation, high-efficiency amplification, realization of the sample-to-result function based on dual-function flow-focusing structure, and accuracy absolute quantification, underscoring its significant potential for ddPCR analysis in clinical diagnostics. [ABSTRACT FROM AUTHOR]

The observed radiation data from the second-generation Hyperspectral Infrared Atmospheric Sounder (HIRAS-II) on the Fengyun-3E (FY-3E) satellite contain useful vertical atmosphere information which can distinguish and retrieve vertical profiles of atmospheric gas components including ozone (O3), carbon monoxide (CO), and methane (CH4). This paper utilizes FY-3E/HIRAS-II observational data to optimize each gas channel using the improved Optimal Sensitivity Profile method (OSP) channel algorithm and establishes a typical convolutional neural network model (CNN) and a representative U-shaped network model (UNET) with deep features and shallow feature links to perform atmospheric profile retrieval calculations of O3, CO, and CH4. We chose the clear sky data of the Indian and its southern seas in December 2021 and January 2022, with reanalysis data from European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and European Center for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis v4 (EAC4) serving as the reference values. The retrieval outcomes were then compared against advanced numerical forecast models including the Whole Atmosphere Community Climate Model (WACCM), Global Forecast System (GFS), and satellite products from an Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). Experimental results show that the generalization ability and retrieval accuracy of CNN are slightly higher compared with UNET. For O3 profile retrieval, the mean percentage error (MPE) of the whole layers for CNN and UNET data in relation to ERA5 data was less than 8%, while the root-mean-square error (RMSE) was below 1.5 × 10−7 kg/kg; for CH4 profile retrieval, the MPE of the whole layers for CNN and UNET data in relation to EAC4 data was less than 0.7%, while the RMSE was below 1.5 × 10−8 kg/kg. The retrieval of O3 and CH4 are resulted in a significant improvement compared to the forecast data and satellite products in most pressure levels; for CO profile retrieval, the MPE of the whole layers for CNN and UNET data in relation to EAC4 data was less than 11%, while the RMSE was below 4 × 10−8 kg/kg. The error of the CO retrieval results was higher than that of the forecast data at the pressure level of 200~500 hPa and lower than that of similar satellite products with most pressure levels. The experiments indicated that the neural network method effectively determines the atmospheric gas profiles using infrared hyperspectral data, exhibiting a positive performance in accuracy and retrieval speed. [ABSTRACT FROM AUTHOR]