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Junctional adhesion molecule-A (JAM-A) is an adhesion molecule that exists on the surface of certain types of cells, including white blood cells, endothelial cells, and dendritic cells. In this study, the cDNA sequences of JAM-A-Fc were chemically synthesized with optimization for mammalian expression. Afterward, we analyzed JAM-A protein expression through transient transfection in HEK293 cell lines. Mice were immunized with JAM-A-Fc protein, and hybridoma was prepared by fusing myeloma cells and mouse spleen cells. Antibodies were purified from the hybridoma supernatant and four monoclonal strains were obtained and numbered 61H9, 70E5, 71A8, and 74H3 via enzyme-linked immunosorbent assay screening. Immunofluorescence staining assay showed 61H9 was the most suitable cell line for mAb production due to its fluorescence signal being the strongest. Flow cytometric analysis proved that 61H9 possessed high affinity. Moreover, antagonism of JAM-A mAb could attenuate the proliferative, migrative, and invasive abilities of ESCC cells and significantly inhibit tumor growth in mice. By examining hematoxylin-eosin staining mice tumor tissues, we found inflammatory cells infiltrated lightly in the anti-JAM-A group. The expression of BCL-2 and IκBα in the anti-JAM-A group were decreased in mice tumor tissues compared to the control group. Ultimately, a method for preparing high-yield JAM-A-Fc protein was created and a high affinity mAb against JAM-A with an antitumor effect was prepared.

A soft fault in an analog circuit is a symptom where the parameter range of a component exists symmetrically to the left and right of its nominal value and exceeds a specific range. The proposed method uses the Grey Wolf Optimization (GWO) optimized tunable Q-factor wavelet transform (TQWT) algorithm for feature refinement, the Inception model for feature extraction, and an SVM for fault diagnosis. First, the Q-factor is optimized to make it more compatible with the signal. Second, the signal is decomposed, and a single-branch reconstruction is performed using the TQWT to extract features adequately. Then, fault feature extraction is conducted using the Inception model to obtain multiscale features. Finally, a Support Vector Machine (SVM) is used to complete the entire fault diagnosis process. The proposed method is comprehensively evaluated using the Sallen–Key bandpass filter circuit and the four-op-amp biquad high-pass filter circuit widely used in electronic systems. The experimental results prove that the proposed method outperforms the existing methods in terms of diagnosis accuracy and reliability.

To accurately locate faulty components in analog circuits, an analog circuit fault diagnosis method based on Tunable Q-factor Wavelet Transform(TQWT) and Convolutional Neural Network (CNN) is proposed in this paper. Firstly, the Grey Wolf algorithm (GWO) is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Secondly, The signal is decomposed, and single-branch reconstruction is conducted with TQWT to facilitate adequate feature extraction. Thirdly, to capture the time-frequency features in the signal, a CNN-LSTM network is built by combining CNN and LSTM for feature extraction. Finally, CNN, which introduces Fully Convolutional Network (FCN) layers and a Batch Normalization layer, is used to fault diagnosis. The method was comprehensively evaluated with a second-order bandpass filter circuit. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault diagnosis accuracy, and the average accuracy is 98.96%.