Studies have shown that exposure to environmental tobacco smoke can increase the risk of bacterial meningitis, and nicotine is the core component of environmental tobacco smoke. Autophagy is an important way for host cells to eliminate invasive pathogens and resist infection. Escherichia coli K1 strain (E. coli K1) is the most common Gram-negative bacterial pathogen that causes neonatal meningitis. The mechanism of nicotine promoting E. coli K1 to invade human brain microvascular endothelial cells (HBMECs), the main component of the blood-brain barrier, is not clear yet. Our study found that the increase of HBMEC autophagy level during E. coli K1 infection could decrease the survival of intracellular bacteria, while nicotine exposure could inhibit the HBMEC autophagic response of E. coli K1 infection by activating the NF-kappa B and PI3K/Akt/mTOR pathway. We concluded that nicotine could inhibit HBMEC autophagy upon E. coli K1 infection and decrease the scavenging effect on E. coli K1, thus promoting the occurrence and development of neonatal meningitis.
Lung nodule detection is of vital importance in the prevention of lung cancer. In the past two decades, most machine learning and deep learning approaches have focused on training models using data collected and stored in centralised data repositories. However, as privacy security becoming more and more important, patient data is scattered in different medical institutions on a small scale and fragmented. In this study, we proposed a federated learning method for training a lung nodule detection model on horizontally distributed data from different clients. In particular, the federated averaging algorithm is used to detect lung nodules by proposing a 3D ResNet18 Dual Path Faster R-CNN model. On this basis, we firstly considered that the quality of the data affects the model training effect. Therefore, we proposed a sampling-based content diversity algorithm that is validated on luna16 data, mitigating model overfitting and improving model generalisation with better results, and also reducing the training time of model. In order to further verify 3D ResNet18 Dual Path Faster R-CNN of federated learning algorithm, we compared it with other federated learning algorithms of deep learning. The experimental results show that the 3D ResNet18 Dual Path Faster R-CNN of federated learning algorithm achieves the best results.
According to indicators, the New V-Rotor Permanent Magnet Motor (NVRPM) for compressors with a rated power of 22 kW and a rated speed of 3000 rpm is optimally designed. Because of the large number of permanent magnets in the V-type PM motor, the magnetic leakage between rotor poles will increase, and its air gap magnetic field contains large harmonic components, which is easy to increase the loss and reduce the efficiency. The structure of the initial V-shaped rotor was optimized by adding multiple air slots and cosine non-uniform air gaps on the rotor surface. Through the finite element simulation of electromagnetic characteristics of NVRPM, it is proved that the improvement of the new motor structure can reduce the loss, weaken the cogging torque, and decrease the torque ripple, and the simulation efficiency is up to 96%, and its operation area is wide and efficient. Finally, the results show that the optimization design of the NVRPM structure is feasible.
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