In this paper, a human electrocardiogram (ECG) identification system based on ensemble empirical mode decomposition (EEMD) is designed. A robust preprocessing method comprising noise elimination, heartbeat normalization and quality measurement is proposed to eliminate the effects of noise and heart rate variability. The system is independent of the heart rate. The ECG signal is decomposed into a number of intrinsic mode functions (IMFs) and Welch spectral analysis is used to extract the significant heartbeat signal features. Principal component analysis is used reduce the dimensionality of the feature space, and the K-nearest neighbors (K-NN) method is applied as the classifier tool. The proposed human ECG identification system was tested on standard MIT-BIH ECG databases: the ST change database, the long-term ST database, and the PTB database. The system achieved an identification accuracy of 95% for 90 subjects, demonstrating the effectiveness of the proposed method in terms of accuracy and robustness.

Purpose Lung adenocarcinoma (LA) is one of the major types of lung cancer. MicroRNAs (miRNAs) play an essential role in regulating responses of natural killer (NK) cells to cancer malignancy. However, the mechanism of miR-218-5p involved in the killing effect of NK cells to LA cells remains poorly understood. Materials and Methods The expression of miR-218-5p was examined by quantitative real-time polymerase chain reaction (qRT-PCR). Serine hydroxymethyl transferase 1 (SHMT1) level was detected by qRT-PCR or western blots. Cytokines production of interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α) were detected by ELISA. The killing effect of NK cells to LA cells was investigated using lactate dehydrogenase cytotoxicity assay kit. The interaction of miR-218-5p and SHMT1 was probed by luciferase activity assay. Xenograft model was established to investigate the killing effect of NK cells in vivo . Results miR-218-5p was enhanced and SHMT1 was inhibited in NK cells of LA patients, whereas stimulation of interleukin-2 (IL-2) reversed their abundances. Addition of miR-218-5p reduced IL-2-induced cytokines expression and cytotoxicity in NK-92 against LA cells. Moreover, SHMT1 was negatively regulated by miR-218-5p and attenuated miR-218-5p-mediated effect on cytotoxicity, IFN-γ and TNF-α secretion in IL-2-activated NK cells. In addition, miR-218-5p exhaustion inhibited tumor growth by promoting killing effect of NK cells. Conclusion miR-218-5p suppresses the killing effect of NK cells to LA cells by targeting SHMT1, providing a potential target for LA treatment by ameliorating NK cells function.

We demonstrate an FPGA implementation of a parallel and reconfigurable architecture for sparse neural networks, capable of on-chip training and inference. The network connectivity uses pre-determined, structured sparsity to significantly reduce complexity by lowering memory and computational requirements. The architecture uses a notion of edge-processing, leading to efficient pipelining and parallelization. Moreover, the device can be reconfigured to trade off resource utilization with training time to fit networks and datasets of varying sizes. The combined effects of complexity reduction and easy reconfigurability enable significantly greater exploration of network hyperparameters and structures on-chip. As proof of concept, we show implementation results on an Artix-7 FPGA.

PurposeTo introduce an end-to-end automatic segmentation model for organs at risk (OARs) in thoracic CT images based on modified DenseNet, and reduce the workload of radiation oncologists.Materials and MethodsThe computed tomography (CT) images of 36 lung cancer patients were included in this study, of which 27 patients’ images were randomly selected as the training set, 9 patients’ as the testing set. The validation set was generated by cross validation and 6 patients’ images were randomly selected from the training set during each epoch as the validation set. The autosegmentation task of the left and right lungs, spinal cord, heart, trachea and esophagus was implemented, and the whole training time was approximately 5 hours. Geometric evaluation metrics including the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD), were used to assess the autosegmentation performance of OARs based on the proposed model and were compared with those based on U-Net as benchmarks. Then, two sets of treatment plans were optimized based on the manually contoured targets and OARs (Plan1), as well as the manually contours targets and the automatically contoured OARs (Plan2). Dosimetric parameters, including Dmax, Dmean and Vx, of OARs were obtained and compared.ResultsThe DSC, HD95 and ASD of the proposed model were better than those of U-Net. The differences in the DSC of the spinal cord and esophagus, differences in the HD95 of the spinal cord, heart, trachea and esophagus, as well as differences in the ASD of the spinal cord were statistically significant between the two models (P<0.05). The differences in the dose-volume parameters of the two sets of plans were not statistically significant (P>0.05). Moreover, compared with manual segmentation, autosegmentation significantly reduced the contouring time by nearly 40.7% (P<0.05).ConclusionsThe bilateral lungs, spinal cord, heart and trachea could be accurately delineated using the proposed model in this study; however, the automatic segmentation effect of the esophagus must still be further improved. The concept of feature map reuse provides a new idea for automatic medical image segmentation.