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

MGTUNet: An new UNet for colon nuclei instance segmentation and quantification

Colorectal cancer (CRC) is among the top three malignant tumor types in terms of morbidity and mortality. Histopathological images are the gold standard for diagnosing colon cancer. Cellular nuclei instance segmentation and classification, and nuclear component regression tasks can aid in the analysis of the tumor microenvironment in colon tissue. Traditional methods are still unable to handle both types of tasks end-to-end at the same time, and have poor prediction accuracy and high application costs. This paper proposes a new UNet model for handling nuclei based on the UNet framework, called MGTUNet, which uses Mish, Group normalization and transposed convolution layer to improve the segmentation model, and a ranger optimizer to adjust the SmoothL1Loss values. Secondly, it uses different channels to segment and classify different types of nucleus, ultimately completing the nuclei instance segmentation and classification task, and the nuclei component regression task simultaneously. Finally, we did extensive comparison experiments using eight segmentation models. By comparing the three evaluation metrics and the parameter sizes of the models, MGTUNet obtained 0.6254 on PQ, 0.6359 on mPQ, and 0.8695 on R2. Thus, the experiments demonstrated that MGTUNet is now a state-of-the-art method for quantifying histopathological images of colon cancer.Comment: Published in BIBM2022(regular paper),https://doi.org/10.1109/BIBM55620.2022.999566

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