Electron Bunch Train Excited Higher-Order Modes in a Superconducting RF Cavity

Higher-order mode (HOM) based intra-cavity beam diagnostics has been proved effectively and conveniently in superconducting radio-frequency (SRF) accelerators. Our recent research shows that the beam harmonics in the bunch train excited HOM spectrum, which have much higher signal-to-noise ratio than the intrinsic HOM peaks, may also be useful for beam diagnostics. In this paper, we will present our study on bunch train excited HOMs, including the theoretic model and recent experiments carried out based on the DC-SRF photoinjector and SRF linac at Peking University.Comment: Supported by National Natural Science Foundation of China (11275014

LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images

Histopathological images are the gold standard for diagnosing liver cancer. However, the accuracy of fully digital diagnosis in computational pathology needs to be improved. In this paper, in order to solve the problem of multi-label and low classification accuracy of histopathology images, we propose a locally deep convolutional Swim framework (LDCSF) to classify multi-label histopathology images. In order to be able to provide local field of view diagnostic results, we propose the LDCSF model, which consists of a Swin transformer module, a local depth convolution (LDC) module, a feature reconstruction (FR) module, and a ResNet module. The Swin transformer module reduces the amount of computation generated by the attention mechanism by limiting the attention to each window. The LDC then reconstructs the attention map and performs convolution operations in multiple channels, passing the resulting feature map to the next layer. The FR module uses the corresponding weight coefficient vectors obtained from the channels to dot product with the original feature map vector matrix to generate representative feature maps. Finally, the residual network undertakes the final classification task. As a result, the classification accuracy of LDCSF for interstitial area, necrosis, non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively. Finally, we use the results of multi-label pathological image classification to calculate the tumor-to-stromal ratio, which lays the foundation for the analysis of the microenvironment of liver cancer histopathological images. Second, we released a multilabel histopathology image of liver cancer, our code and data are available at https://github.com/panliangrui/LSF.Comment: Submitted to BIBM202

CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images

Histopathology image segmentation is the gold standard for diagnosing cancer, and can indicate cancer prognosis. However, histopathology image segmentation requires high-quality masks, so many studies now use imagelevel labels to achieve pixel-level segmentation to reduce the need for fine-grained annotation. To solve this problem, we propose an attention-based cross-view feature consistency end-to-end pseudo-mask generation framework named CVFC based on the attention mechanism. Specifically, CVFC is a three-branch joint framework composed of two Resnet38 and one Resnet50, and the independent branch multi-scale integrated feature map to generate a class activation map (CAM); in each branch, through down-sampling and The expansion method adjusts the size of the CAM; the middle branch projects the feature matrix to the query and key feature spaces, and generates a feature space perception matrix through the connection layer and inner product to adjust and refine the CAM of each branch; finally, through the feature consistency loss and feature cross loss to optimize the parameters of CVFC in co-training mode. After a large number of experiments, An IoU of 0.7122 and a fwIoU of 0.7018 are obtained on the WSSS4LUAD dataset, which outperforms HistoSegNet, SEAM, C-CAM, WSSS-Tissue, and OEEM, respectively.Comment: Submitted to BIBM202

pN1 but not pN0/N2 predicts survival benefits of prophylactic cranial irradiation in small-cell lung cancer patients after surgery.

Background Prophylactic cranial irradiation has been shown to reduce brain metastases and provide survival benefits in small-cell lung cancer (SCLC). However, its role in limited-stage SCLC patients after surgery remains unclear. Further, it is unknown whether the effect of prophylactic cranial irradiation is generalizable in these patients with different pathological nodal (N0-N2) stages, a state indicating the presence of tumor metastases. Methods We combined data from a single medical center and Surveillance, Epidemiology, and End Results database. Propensity score matching analyses were performed (1:2) to evaluate the role of prophylactic cranial irradiation in SCLC patients after surgery. Cox proportional hazards regression model was used to identify predictors of survival. Results 124 (18.7%) out of 664 surgically-treated SCLC patients received prophylactic cranial irradiation treatment. Within the entire cohort, multivariate Cox regression analysis identified dataset source, age, pathological T and N stages, adjuvant chemotherapy, resection type, and histology as independent prognostic factors for overall survival. Prophylactic cranial irradiation appeared to be associated with a better overall survival, but the difference is marginally significant (P=0.063). Further, we stratified patients based on the pathological N0-N2 stages using propensity score matching analyses, which showed that prophylactic cranial irradiation treatment was superior to non-prophylactic cranial irradiation treatment for surgically-treated SCLC patients with N1 stage only (univariate analysis: P=0.026; multivariate Cox: P=0.004), but not N0/N2 stage (univariate analysis: P=0.65 and P=0.28, respectively; multivariate Cox: P=0.99 and P=0.35, respectively). Conclusions Prophylactic cranial irradiation provides survival benefits for SCLC patients with pN1 after surgery but not with pathological N0/N2 stage. Our findings may provide helpful stratifications for clinical decision-making of prophylactic cranial irradiation intervention in SCLC patients

Self-Lubricating Polytetrafluoroethylene/Polyimide Blends Reinforced with Zinc Oxide Nanoparticles

ZnO nanoparticle reinforced polytetrafluoroethylene/polyimide (PTFE/PI) nanocomposites were prepared and their corresponding tribological and mechanical properties were studied in this work. The influences of ZnO loading, sliding load, and velocity on the tribological properties of ZnO/PTFE/PI nanocomposites were systematically investigated. Results reveal that nanocomposites reinforced with 3 wt% ZnO exhibit the optimal tribological and mechanical properties. Specifically, the wear loss decreased by 20% after incorporating 3 wt% ZnO compared to unfilled PTFE/PI. Meanwhile, the impact strength, tensile strength, and elongation-at-break of 3 wt% ZnO/PTFE/PI nanocomposite are enhanced by 85, 5, and 10% compared to pure PTFE/PI blend. Microstructure investigation reveals that ZnO nanoparticles facilitate the formation of continuous, uniform, and smooth transfer film and thus reduce the adhesive wear of PTFE/PI

Multi-Head Attention Mechanism Learning for Cancer New Subtypes and Treatment Based on Cancer Multi-Omics Data

Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omics data and clinical features among subtypes of different cancers. Therefore, the identification and discovery of cancer subtypes are crucial for the diagnosis, treatment, and prognosis of cancer. In this study, we proposed a generalization framework based on attention mechanisms for unsupervised contrastive learning (AMUCL) to analyze cancer multi-omics data for the identification and characterization of cancer subtypes. AMUCL framework includes a unsupervised multi-head attention mechanism, which deeply extracts multi-omics data features. Importantly, a decoupled contrastive learning model (DMACL) based on a multi-head attention mechanism is proposed to learn multi-omics data features and clusters and identify new cancer subtypes. This unsupervised contrastive learning method clusters subtypes by calculating the similarity between samples in the feature space and sample space of multi-omics data. Compared to 11 other deep learning models, the DMACL model achieved a C-index of 0.002, a Silhouette score of 0.801, and a Davies Bouldin Score of 0.38 on a single-cell multi-omics dataset. On a cancer multi-omics dataset, the DMACL model obtained a C-index of 0.016, a Silhouette score of 0.688, and a Davies Bouldin Score of 0.46, and obtained the most reliable cancer subtype clustering results for each type of cancer. Finally, we used the DMACL model in the AMUCL framework to reveal six cancer subtypes of AML. By analyzing the GO functional enrichment, subtype-specific biological functions, and GSEA of AML, we further enhanced the interpretability of cancer subtype analysis based on the generalizable AMUCL framework