Expression of GCRG213p, LINE-1 endonuclease variant, significantly different in gastric complete and incomplete intestinal metaplasia.

BACKGROUND: Intestinal metaplasia (IM) of the gastric mucosa is classified as complete (Type I) and incomplete IM (Type II and III) subtypes, which showed significantly different risk for developing to gastric adenocarcinoma (GAC). GCRG213, a variant of L1-endonuclease (L1-EN), first identified in our lab, was upregulated in GAC tissue. However, the relationship between GCRG213 and IM subtypes is not clear. Our study explored the association of GCRG213 protein (GCRG213p) with IM subtypes. METHODS: Gastric cancer and/or para-tumor tissue samples were collected from 123 patients who underwent gastrectomy for intestinal type gastric adenocarcinoma. The subtypes of IM were characterized with Alcian blue-periodic acid-Schiff and High Iron Diamine-Alcian blue staining methods. Immunohistochemistry of GCRG213p was performed, and its expression in gastric adenocarcinoma and para-tumor tissue including dysplasia, IM, and normal mucosa were analyzed. RESULTS: GCRG213p was expressed in 48.94% IM, 57.14% dysplasia and 55.32% GAC, respectively. GCRG213p expression was higher in well and moderately differentiated adenocarcinoma (P = 0.037). In IM glands, GCRG213p expressed mainly in the cytoplasm of absorptive enterocytes with defined brush borders, but not in goblet cells. The expression of GCRG213p in type I IM (90.00%) was significantly higher than that in type II (36.36%) and type III (25.00%) (P \u3c 0.001). In normal gastric mucosa, GCRG213p was exclusively positive in the cytoplasm of gastric chief cells. CONCLUSIONS: The expression of GCRG213p in complete IM was significantly higher than in incomplete IM, which implies that GCRG213p may play a role on the developing of IM to adenocarcinoma. GCRG213p was exclusively expressed in chief cells, suggesting that it might be involved in cell differentiation from the chief cells to IM

Overview and prospect of the detection capability of China's first precipitation measurement satellite FY-3G

Based on introducing the technical characteristics of FY-3G, which is China's first precipitation measurement satellite and successfully launched at 09∶36 BT on April 16 in 2023, this paper focuses on the precipitation detection capabilities and application prospect in rainstorm monitoring of FY-3G. The results show that, with an orbit at 407 km and an inclination angle of 50°, and equipped with a dual-frequency Ka/Ku band precipitation measurement radar, microwave, and optical imaging instruments, the FY-3G satellite can detect the three-dimensional structure of disastrous weather systems such as typhoon, heavy rainfall, and other strong convection events in most of China. At the design level, FY-3G has precipitation detection capabilities comparable to the current US Second Generation Global Precipitation Measurement Program (GPM) Core Satellite (GPMCO), but better payload types, quantities, and channel settings compared with the GPMCO satellite. After the service operation, the FY-3G satellite, together with other polar-orbiting meteorological satellites such as FY-3 AM, PM, and EM, as well as high-orbit geostationary satellites, will form the Fengyun precipitation detection constellation system, which will improve the overall precipitation detection capability of the Fengyun Satellite constellation and provide stronger basic support for meteorological disaster prevention and mitigation

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Combining the Finite Element Analysis and Kriging Model for Study on Laser Surface Hardening Parameters of Pitch Bearing Raceway

Laser surface hardening is used to improve the fatigue performance of the large diameter pitch bearing. Determination of the process parameters by a trial and error method, depending on the experience of the technician, by changing the parameters repeatedly for each laser surface hardening process is time-consuming and costly. In this paper, a method of analyzing the maximum temperature and depth of a hardened layer during the laser surface hardening process for a pitch bearing raceway of a wind turbine is proposed, which combines finite element simulation and the Kriging model. A three-dimensional finite element model of a pitch bearing ring was established using ABAQUS. The temperature field analysis was performed. The effects of process parameters including laser power, scanning speed, and laser spot radius on the depth of the hardening layer were investigated. Then, taking into account the interactional effects of different process parameters, Kriging models were constructed to reflect the relationship between input process parameters and output responses. The results show that the Kriging approximation model has a small relative error compared with the simulated results and can be used to predict the hardened layer depth

A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization

Automatic image annotation is for more accurate image retrieval and classification by assigning labels to images. This paper proposes a semisupervised framework based on graph embedding and multiview nonnegative matrix factorization (GENMF) for automatic image annotation with multilabel images. First, we construct a graph embedding term in the multiview NMF based on the association diagrams between labels for semantic constraints. Then, the multiview features are fused and dimensions are reduced based on multiview NMF algorithm. Finally, image annotation is achieved by using the new features through a KNN-based approach. Experiments validate that the proposed algorithm has achieved competitive performance in terms of accuracy and efficiency