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Electrical impedance flow cytometry (EIFC) based on microfluidic chip has been applied to the analysis of cells and micron particles, but the sensitivity needs further optimization for bacterial detections. In this paper, we propose an optimization of buffer conductivity to improve the sensitivity of EIFC. In this paper, polystyrene microspheres and yeast were suspended in buffers with different conductivity and detected by EIFC chip respectively. After analysing the signal-to-noise ratio (SNR) and coefficient of variation (CV) of the data, the optimum buffer conductivities for detection were obtained for Polystyrene microspheres (~1.6 S/m) and bacteria (~6.4 S/m), respectively. Then using this optimized microfluidic detection system, the O1.0 and O0.8 μm microspheres, Staphylococcus aureus and Staphylococcus albicans were analyzed, respectively. The results proved that the sensitivity of the EIFC chip can be improved by optimizing the buffer conductivity, which promotes a foundation for the detection, differentiation and analysis of submicron particles/bacteria in the future.

This paper aims to solve the problem of automatic detection of rice leaf lesions in natural scenes using deep learning techniques. In this paper, the Linknet full convolutional network was built to train the segmentation model. The network compensates the lost spatial information in the feature extraction process through the short connection structure between downsampling and corresponding upsampling. The model takes rice canopy RGB image as input and then output binarized lesion segmentation image. Then considered with the distribution characteristics of lesion spots, the loss function of the origin model was replaced with Focal loss function, which further improved the segmentation accuracy of the model. The average precision and recall have respectively achieved 98.55% and 98.64% on validate data set, and the average false positive rate has reduced to 1.36%, which has a better segmentation performance. It creates a good precondition for automatic identification of leaves diseases.

Background and Purpose Targeted therapy has led to dramatic change in the treatment of lung adenocarcinoma, but lung squamous cell carcinoma(LSCC) lacks targeted therapy options. High-frequency somatic mutations in KEAP1/NRF2 (27.9%) have been identified in LSCC. In this study, we explored the role of KEAP1 somatic mutations in the development of LSCC and whether a nuclear factor erythroid 2-related factor 2(NRF2) inhibitor be potential to target lung cancer carrying KEAP1/NRF2 mutations.Methods Lung cancer cell lines A549 and H460 with loss-of-function mutations in KEAP1 stably transfected with wild-type (WT) KEAP1 or somatic mutations in KEAP1 were used to investigate the functions of somatic mutations in KEAP1 .Flow cytometry,plate clone formation experiments,and scratch tests were used to examine reactive oxygen species, proliferation, and migration of these cell lines.Results The expression of NRF2 and its target genes increased, and tumor cell proliferation, migration, and tumor growth were accelerated in A549 and H460 cells stably transfected with KEAP1 mutants compared to control cells with a loss-of-function KEAP1 mutation and stably transfected with WT KEAP1 in both in vitro and in vivo studies. Inhibited proliferation was more apparent in the A549 cell line trasfected with the R320Q KEAP1 mutant than the A549 cell line trasfected with WT KEAP1 after treatment with NRF2 inhibitor ML385.Conclusion Somatic mutations in KEAP1 identified from patients with lung carcinoma likely promote tumorigenesis mediated by activation of the KEAP1/NRF2 antioxidant stress response pathway. NRF2 inhibition with ML385 could inhibit the proliferation of tumor cells with KEAP1 mutation.